The digital and energy transition will change our industry. To be prepared for this challenge, NHL Stenden University of Applied Sciences puts quite some effort in developing new innovative courses and new types of digitally enabled education. An example is a new minor concentrated on engineering tools and methods that have emerged in the (construction) industry over the past decades. In addition, the school is also working on a game to educate an old trade: ship stability. In addition to the changes to the existing programs, a new level of education is introduced in the Netherlands, the Professional Doctorate. The Professional Doctorate is comparable to the PhD but focusses on practically applied research. All these changes and innovations to the current maritime education at NHL Stenden are elaborated upon in this paper. The paper concludes with an outlook to the future, based upon the results from a survey held under students and lecturers regarding their view on the future of maritime education. The results of this survey show that especially green and modern propulsion methods are underexposed in the current curriculum.

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In courses of ship design and engineering the particulars of the profession are well-taught. However, driven by software and computer advancement, in the industry over the past decades new tools have emerged, such as optimization, geometric modelling, CFD, big data and machine learning. These tools have been considered too complex for an undergraduate program. Yet, some knowledge of this trade is essential on every professional level, and our proposition is that if the material is offered in a first-principle fashion, in combination with practical exercises and oral discussion, the heart of the matter can very well be educated to an undergraduate. Recently, a 30 EC minor was given in this fashion.@en

In the shipping industry power prediction methods are commonly used. An option is to predict the power based with a theoretical analysis. However, with a purely theoretical approach it is not possible to evaluate all operating conditions. The second, simulation methods, are able to describe all the necessary quantities in detail. Nonetheless, simulation requires relatively high computational power. Thus, the current power prediction methods used in the shipping industry are insufficiently all-encompassing or accessible. Therefore, a machine learning approach is developed to calculate the ships speed over ground using neural network and convolutional neural network techniques. For training and validation of the model operational data from a fall-pipe vessel is used. The developed method combined with ship motion could result in an optimal power usage, and thus leads to reduced fuel consumption and emissions. The method could also be used for optimised routing. Although in this case study applied to one single vessel, the developed model is generally applicable, providing ship management companies the possibility to train the model with operational data from their fleet, therewith, offering the possibility of reduced fuel consumption and thus emissions on a global level.@en

In recent years, a significant amount of research has been conducted on autonomous ships. Since it is assumed that these ships will sail with a significantly reduced crew or even without people on board, the design of the ship needs reconsideration. The absence of people on board and the associated safety measures could result in a more efficient design, but amendments in the existing regulatory framework will be needed. In this article, we will focus on potential changes in the Convention for Safety Of Life At Sea (SOLAS) and in particular on the Required Subdivision Index. The index gives a requirement for the allowed probability of sinking when a ship is damaged due to collision. The evaluation is performed by using the principle of equivalent safety, which will ensure that unmanned ships will be at least as safe as manned ships. If the crew is no longer present, the consequences of an incident will be less severe, since the probability of casualties is no longer present. Consequently, a lower subdivision index might be accepted for unmanned autonomous ships. In this article, the damage stability-related level of risk of a manned ship will be derived by means of a risk analysis. Thereafter, the subdivision index for unmanned ships, which ensures an equivalent safety level to similar manned ships, is established for three individual ships. The assessment shows that a reduction in the subdivision index is allowed for unmanned ships and that the reduction will be largest for smaller ships.

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